The field of autonomous vehicles (AV) has been the subject of extensive research in recent
years. It is possible that Avs could contribute greatly to the quality of daily lives if they were …

While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …

Safe decision-making strategy of autonomous vehicles (AVs) plays a critical role in avoiding
accidents. This study develops a safe reinforcement learning (safe-RL)-based driving policy …

Reinforcement learning has gradually demonstrated its decision-making ability in
autonomous driving. Reinforcement learning is learning how to map states to actions by …

Reinforcement learning is considered one of the most promising approaches for decision-
making in autonomous vehicles within interactive scenarios. However, its implementation …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot
guarantee the agent's safety in the training process due to the requirements of interaction …

Reinforcement learning (RL) provides a compelling framework for enabling autonomous
vehicles to continue to learn and improve diverse driving behaviors on their own. However …

Reinforcement Learning (RL) algorithms show amazing performance in recent years, but
placing RL in real-world applications such as self-driven vehicles may suffer safety …

Autonomous driving is a research field that has received attention in recent years, with
increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an …